data_1 = [[1.32] [1.32] [1.32]] data_2= [[2.32] [2.32] [2.32]] 将 data_1和 data_2填加到一个dateframe中
时间: 2023-08-02 14:11:31 浏览: 32
可以使用 pandas 库中的 DataFrame 类来实现:
```python
import pandas as pd
data_1 = [[1.32], [1.32], [1.32]]
data_2 = [[2.32], [2.32], [2.32]]
df = pd.DataFrame({'data_1': data_1, 'data_2': data_2})
```
这样就可以将 data_1 和 data_2 合并成一个 DataFrame,其中 'data_1' 和 'data_2' 是列名。可以使用 `print(df)` 打印出 DataFrame 的内容。
相关问题
SELECT DISTINCT ( A.DATA_TYPE ) AS DATA_TYPE, A.DATA_VALUE AS TSL, IFNULL(( SELECT B.DATA_VALUE FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA B WHERE B.IS_DELETED = 0 AND B.DATA_DIMENSION = '期末' AND B.LEVEL = 2 AND B.DATA_NAME = '举报' AND B.DATA_AREA = '市级' AND B.DATA_TYPE = A.DATA_TYPE AND B.DATA_TIME = ( SELECT MAX( DATA_TIME ) FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA WHERE IS_DELETED = 0 AND DATA_DIMENSION = '期末' AND DATA_NAME = '举报' AND LEVEL = 2 AND DATA_AREA = '市级' ) ), 0 ) AS JBL, CURRENT_ID FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA A WHERE A.IS_DELETED = 0 AND A.DATA_DIMENSION = '期末' AND A.LEVEL = 2 AND A.DATA_NAME = '投诉' AND A.DATA_AREA = '市级' AND A.DATA_TIME = ( SELECT MAX( DATA_TIME ) FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA WHERE IS_DELETED = 0 AND DATA_DIMENSION = '期末' AND DATA_NAME = '投诉' AND LEVEL = 2 AND DATA_AREA = '市级')这个sql 优化
对于这个SQL查询语句的优化,可以考虑以下几点:
1. 使用JOIN语句代替子查询:将子查询转换为JOIN语句,可以提高查询性能。
2. 添加适当的索引:根据查询条件和连接条件,为相关表添加合适的索引,以加快查询速度。
3. 减少重复的条件判断:在子查询和主查询中,有一些条件判断是重复的,可以将它们提取出来,避免重复计算。
4. 使用表别名:为表和字段使用简短的别名,提高查询语句的可读性。
下面是对原始SQL查询语句进行优化的示例:
SELECT DISTINCT
A.DATA_TYPE AS DATA_TYPE,
A.DATA_VALUE AS TSL,
IFNULL(B.DATA_VALUE, 0) AS JBL,
A.CURRENT_ID
FROM
YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA A
LEFT JOIN
(
SELECT
DATA_TYPE, DATA_VALUE
FROM
YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA
WHERE
IS_DELETED = 0
AND DATA_DIMENSION = '期末'
AND DATA_NAME = '举报'
AND LEVEL = 2
AND DATA_AREA = '市级'
AND DATA_TIME = (
SELECT MAX(DATA_TIME)
FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA
WHERE
IS_DELETED = 0
AND DATA_DIMENSION = '期末'
AND DATA_NAME = '举报'
AND LEVEL = 2
AND DATA_AREA = '市级'
)
) B ON A.DATA_TYPE = B.DATA_TYPE
WHERE
A.IS_DELETED = 0
AND A.DATA_DIMENSION = '期末'
AND A.LEVEL = 2
AND A.DATA_NAME = '投诉'
AND A.DATA_AREA = '市级'
AND A.DATA_TIME = (
SELECT MAX(DATA_TIME)
FROM YXDDZH_MIDDLE.GZ_SCREEN_JGSY_SDHJ_SPFW_CITY_AREA_DATA
WHERE
IS_DELETED = 0
AND DATA_DIMENSION = '期末'
AND DATA_NAME = '投诉'
AND LEVEL = 2
AND DATA_AREA = '市级'
);
请注意,具体的优化策略可能需要根据实际情况进行调整和测试,以达到最佳的查询性能。
请帮我解读这段代码if __name__ == "__main__": data_split_train_val_test(data_root='data', data_set='human') data_split_train_val_test(data_root='data', data_set='celegans') GNNDataset(root='data/human') GNNDataset(root='data/celegans')
这段代码中的`if __name__ == "__main__"`条件语句用于判断当前模块是否作为主程序运行。
在这段代码中,如果当前模块是作为主程序运行的,将会执行以下操作:
1. 调用`data_split_train_val_test`函数,传入参数`data_root='data'`和`data_set='human'`,进行数据集的训练、验证和测试集划分操作。
2. 调用`data_split_train_val_test`函数,传入参数`data_root='data'`和`data_set='celegans'`,进行数据集的训练、验证和测试集划分操作。
3. 创建`GNNDataset`对象,传入参数`root='data/human'`,用于处理名为'human'的数据集。
4. 创建`GNNDataset`对象,传入参数`root='data/celegans'`,用于处理名为'celegans'的数据集。
总之,这段代码的作用是在当前模块作为主程序运行时执行一些特定的操作,包括数据集的划分和创建相关对象。